Data-driven Patient-Specific Agent Based Models of Metastatic Melanoma for Immunotherapy Response Prediction

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Overall Project Summary The proposed U54 program Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor -- Immunotherapy Combinations (ST-Analytics) is designed to develop the recent conceptual advance that targeted inhibitor + cancer immunotherapy (IT) combination treatments may yield significantly greater patient benefit if those treatments are administered in sequence rather than simultaneously. Analysis of retrospective clinical data coupled with in vivo therapeutic modeling using syngeneic models of murine melanoma strongly support this concept. In fact, the picture that has emerged in melanoma is that immune factors can play a strong role in driving resistance to MAPK inhibitor (MAPKi) therapy, and that lead-in immune checkpoint blockade (ICB) can ‘prime’ both the primary tumor and distal metastases (including brain metastases) for eradication when the IT is subsequently combined with MAPKi. This observation opens the doors for immune based strategies, such as ICB or adoptive cell therapy (ACT), as sequential combinatorial agents to prevent MAPKi resistance. However, this concept introduces a number of new variables, including dosing, sequence, and timing. This can make the design and execution of clinical trials that can yield statistically significant outcomes impractical. This is the scientific and translational problem we address in the proposed ST-Analytics U54. The ST-Analytics U54 center is populated by leading scientists at the ISB, the UCLA Geffen School of Medicine, and Yale, and is comprised of two research projects and two research cores, with each project integrating both state-of-the-art experimentation and computational work. This structure is further designed to bring together the scientific, experimental, and computational and administrative resources to develop a data base that captures the kinetics of lead-in monotherapy tumor priming, and apply that data base to the development of predictive in silico models that can inform the design of such targeted inhibitor – immunotherapy sequence combinations for clinical trials. This requires close integration and cycles of iteration between of state-of-the-art experimentation, leading edge computation, and realistic disease models, continuously calibrated through the analysis of highly relevant, biopsied patient tumors. The resulting science also provides exciting opportunities for high impact STEM outreach. We propose to act on those opportunities by leveraging a long-standing systems education outreach program at ISB that already has impacted K-12 STEM education in all 50 states, and places an emphasis on those communities that have been historically under-represented in STEM.